OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation

• URL: http://arxiv.org/abs/2103.14283v1
• Date: Fri, 26 Mar 2021 06:11:05 GMT
• Title: OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
• Authors: Yifan You, Lin Shao, Toki Migimatsu, Jeannette Bohg
• Abstract summary: We propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably. Our system learns to estimate the contact point correspondences between the object and supporting item to get an estimated stable pose. Then, the robot needs to find a collision-free path to move the object from its initial pose to stable hanging pose.
• Score: 14.989379991558046
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability has applications in many domains such as domestic services, logistics, or manufacturing. Yet, it is a challenging manipulation task due to the large diversity of geometry and topology of everyday objects. In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably. Our system learns to estimate the contact point correspondences between the object and supporting item to get an estimated stable pose. We then run a deep reinforcement learning algorithm to refine the predicted stable pose. Then, the robot needs to find a collision-free path to move the object from its initial pose to stable hanging pose. To this end, we train a neural network based collision estimator that takes as input partial point clouds of the object and supporting item. We generate a new and challenging, large-scale, synthetic dataset annotated with stable poses of objects hung on various supporting items and their contact point correspondences. In this dataset, we show that our system is able to achieve a 68.3% success rate of predicting stable object poses and has a 52.1% F1 score in terms of finding feasible paths. Supplemental material and videos are available on our project webpage.

Related papers

• 3D Foundation Models Enable Simultaneous Geometry and Pose Estimation of Grasped Objects [13.58353565350936]
  We contribute methodology to jointly estimate the geometry and pose of objects grasped by a robot. Our method transforms the estimated geometry into the robot's coordinate frame. We empirically evaluate our approach on a robot manipulator holding a diverse set of real-world objects.
  arXiv  Detail & Related papers  (2024-07-14T21:02:55Z)
• ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors [73.26004792375556]
  This paper shows that robustness and generalisation to novel scene objects in 3D object-aware character synthesis can be achieved by training a motion model with as few as one reference object. We leverage an implicit feature representation trained on object-only datasets, which encodes an SE(3)-equivariant descriptor field around the object. We demonstrate substantial improvements in 3D virtual character motion and interaction quality and robustness to scenarios with unseen objects.
  arXiv  Detail & Related papers  (2023-08-24T17:59:51Z)
• HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions [17.9178233068395]
  We present the HANDAL dataset for category-level object pose estimation and affordance prediction. The dataset consists of 308k annotated image frames from 2.2k videos of 212 real-world objects in 17 categories. We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks.
  arXiv  Detail & Related papers  (2023-08-02T23:59:59Z)
• DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations [51.87067543670535]
  We propose a robot-learning system that can take a small number of human demonstrations and learn to grasp unseen object poses. We train a dexterous grasping policy that takes the point clouds of the object as input and predicts continuous actions to grasp objects from different initial robot states. The policy learned from our dataset can generalize well on unseen object poses in both simulation and the real world.
  arXiv  Detail & Related papers  (2022-09-28T17:51:49Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis [78.26022688167133]
  We present a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis. The proposed dataset contains 100,000 images and 25 different object types. We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance.
  arXiv  Detail & Related papers  (2021-12-29T17:23:24Z)
• DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration [42.19014385637538]
  We propose to teach a robot how to grasp an object with a simple and short human demonstration. We first present a small sequence of RGB-D images displaying a human-object interaction. This sequence is then leveraged to build associated hand and object meshes that represent the interaction.
  arXiv  Detail & Related papers  (2021-12-06T08:17:12Z)
• Learning to Regrasp by Learning to Place [19.13976401970985]
  Regrasping is needed when a robot's current grasp pose fails to perform desired manipulation tasks. We propose a system for robots to take partial point clouds of an object and the supporting environment as inputs and output a sequence of pick-and-place operations. We show that our system is able to achieve 73.3% success rate of regrasping diverse objects.
  arXiv  Detail & Related papers  (2021-09-18T03:07:06Z)
• INVIGORATE: Interactive Visual Grounding and Grasping in Clutter [56.00554240240515]
  INVIGORATE is a robot system that interacts with human through natural language and grasps a specified object in clutter. We train separate neural networks for object detection, for visual grounding, for question generation, and for OBR detection and grasping. We build a partially observable Markov decision process (POMDP) that integrates the learned neural network modules.
  arXiv  Detail & Related papers  (2021-08-25T07:35:21Z)
• Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping [0.0]
  We propose a system that performs real-time object detection and pose estimation, for the purpose of dynamic robot grasping. The proposed approach allows the robot to detect the object identity and its actual pose, and then adapt a canonical grasp in order to be used with the new pose. For training, the system defines a canonical grasp by capturing the relative pose of an object with respect to the gripper attached to the robot's wrist. During testing, once a new pose is detected, a canonical grasp for the object is identified and then dynamically adapted by adjusting the robot arm's joint angles.
  arXiv  Detail & Related papers  (2021-01-18T22:22:47Z)
• Reactive Human-to-Robot Handovers of Arbitrary Objects [57.845894608577495]
  We present a vision-based system that enables human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects.
  arXiv  Detail & Related papers  (2020-11-17T21:52:22Z)
• Occlusion resistant learning of intuitive physics from videos [52.25308231683798]
  Key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these physical rules from video sequences.
  arXiv  Detail & Related papers  (2020-04-30T19:35:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.